


'''simple docstring'''



import argparse

import torch
from datasets import load_dataset
from donut import DonutModel

from transformers import (
    DonutImageProcessor,
    DonutProcessor,
    DonutSwinConfig,
    DonutSwinModel,
    MBartConfig,
    MBartForCausalLM,
    VisionEncoderDecoderModel,
    XLMRobertaTokenizerFast,
)







def      _UpperCamelCase   (		UpperCamelCase__       ):
			UpperCAmelCase__	:       Optional[Any]   =  model.config

			UpperCAmelCase__	:       Optional[int]   =  DonutSwinConfig(
			    image_size=original_config.input_size			,   patch_size=4			,   depths=original_config.encoder_layer			,   num_heads=[4, 8, 1_6, 3_2]			,   window_size=original_config.window_size			,   embed_dim=1_2_8			,   )
			UpperCAmelCase__	:       Union[str, Any]   =  MBartConfig(
			    is_decoder=lowerCamelCase_			,   is_encoder_decoder=lowerCamelCase_			,   add_cross_attention=lowerCamelCase_			,   decoder_layers=original_config.decoder_layer			,   max_position_embeddings=original_config.max_position_embeddings			,   vocab_size=len(
			        model.decoder.tokenizer       )			,   scale_embedding=lowerCamelCase_			,   add_final_layer_norm=lowerCamelCase_			,   )

			return encoder_config, decoder_config







def      _UpperCamelCase   (		UpperCamelCase__       ):
			if "encoder.model" in name:
						UpperCAmelCase__	:       Union[str, Any]   =  name.replace("""encoder.model"""			,   """encoder"""       )
			if "decoder.model" in name:
						UpperCAmelCase__	:       Optional[int]   =  name.replace("""decoder.model"""			,   """decoder"""       )
			if "patch_embed.proj" in name:
						UpperCAmelCase__	:       Union[str, Any]   =  name.replace("""patch_embed.proj"""			,   """embeddings.patch_embeddings.projection"""       )
			if "patch_embed.norm" in name:
						UpperCAmelCase__	:       int   =  name.replace("""patch_embed.norm"""			,   """embeddings.norm"""       )
			if name.startswith("""encoder"""       ):
						if "layers" in name:
									UpperCAmelCase__	:       Tuple   =  '''encoder.''' + name
						if "attn.proj" in name:
									UpperCAmelCase__	:       str   =  name.replace("""attn.proj"""			,   """attention.output.dense"""       )
						if "attn" in name and "mask" not in name:
									UpperCAmelCase__	:       int   =  name.replace("""attn"""			,   """attention.self"""       )
						if "norm1" in name:
									UpperCAmelCase__	:       int   =  name.replace("""norm1"""			,   """layernorm_before"""       )
						if "norm2" in name:
									UpperCAmelCase__	:       List[Any]   =  name.replace("""norm2"""			,   """layernorm_after"""       )
						if "mlp.fc1" in name:
									UpperCAmelCase__	:       Union[str, Any]   =  name.replace("""mlp.fc1"""			,   """intermediate.dense"""       )
						if "mlp.fc2" in name:
									UpperCAmelCase__	:       List[str]   =  name.replace("""mlp.fc2"""			,   """output.dense"""       )

						if name == "encoder.norm.weight":
									UpperCAmelCase__	:       int   =  '''encoder.layernorm.weight'''
						if name == "encoder.norm.bias":
									UpperCAmelCase__	:       Optional[int]   =  '''encoder.layernorm.bias'''

			return name







def      _UpperCamelCase   (		UpperCamelCase__			,   UpperCamelCase__       ):
			for key in orig_state_dict.copy().keys():
						UpperCAmelCase__	:       List[str]   =  orig_state_dict.pop(lowerCamelCase_       )

						if "qkv" in key:
									UpperCAmelCase__	:       List[str]   =  key.split("""."""       )
									UpperCAmelCase__	:       Tuple   =  int(key_split[3]       )
									UpperCAmelCase__	:       Tuple   =  int(key_split[5]       )
									UpperCAmelCase__	:       Union[str, Any]   =  model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

									if "weight" in key:
												UpperCAmelCase__	:       str   =  val[:dim, :]
												UpperCAmelCase__	:       str   =  val[dim : dim * 2, :]
												UpperCAmelCase__	:       List[Any]   =  val[-dim:, :]
									else:
												UpperCAmelCase__	:       Optional[Any]   =  val[:dim]
												UpperCAmelCase__	:       Dict   =  val[dim : dim * 2]
												UpperCAmelCase__	:       Union[str, Any]   =  val[-dim:]
						elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
									# HuggingFace implementation doesn't use attn_mask buffer
									# and model doesn't use final LayerNorms for the encoder
									pass
						else:
									UpperCAmelCase__	:       Optional[Any]   =  val

			return orig_state_dict







def      _UpperCamelCase   (		UpperCamelCase__			,   UpperCamelCase__=None			,   UpperCamelCase__=False       ):
			# load original model
			UpperCAmelCase__	:       List[Any]   =  DonutModel.from_pretrained(lowerCamelCase_       ).eval()

			# load HuggingFace model
			UpperCAmelCase__	:       List[Any]   =  get_configs(lowerCamelCase_       )
			UpperCAmelCase__	:       List[Any]   =  DonutSwinModel(lowerCamelCase_       )
			UpperCAmelCase__	:       Optional[Any]   =  MBartForCausalLM(lowerCamelCase_       )
			UpperCAmelCase__	:       Dict   =  VisionEncoderDecoderModel(encoder=lowerCamelCase_			,   decoder=lowerCamelCase_       )
			model.eval()

			UpperCAmelCase__	:       Any   =  original_model.state_dict()
			UpperCAmelCase__	:       Union[str, Any]   =  convert_state_dict(lowerCamelCase_			,   lowerCamelCase_       )
			model.load_state_dict(lowerCamelCase_       )

			# verify results on scanned document
			UpperCAmelCase__	:       str   =  load_dataset("""hf-internal-testing/example-documents"""       )
			UpperCAmelCase__	:       str   =  dataset['''test'''][0]['''image'''].convert("""RGB"""       )

			UpperCAmelCase__	:       Optional[Any]   =  XLMRobertaTokenizerFast.from_pretrained(lowerCamelCase_			,   from_slow=lowerCamelCase_       )
			UpperCAmelCase__	:       Any   =  DonutImageProcessor(
			    do_align_long_axis=original_model.config.align_long_axis			,   size=original_model.config.input_size[::-1]       )
			UpperCAmelCase__	:       int   =  DonutProcessor(lowerCamelCase_			,   lowerCamelCase_       )
			UpperCAmelCase__	:       Optional[int]   =  processor(lowerCamelCase_			,   return_tensors="""pt"""       ).pixel_values

			if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
						UpperCAmelCase__	:       Union[str, Any]   =  '''<s_docvqa><s_question>{user_input}</s_question><s_answer>'''
						UpperCAmelCase__	:       Union[str, Any]   =  '''When is the coffee break?'''
						UpperCAmelCase__	:       str   =  task_prompt.replace("""{user_input}"""			,   lowerCamelCase_       )
			elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
						UpperCAmelCase__	:       Optional[Any]   =  '''<s_rvlcdip>'''
			elif model_name in [
			    "naver-clova-ix/donut-base-finetuned-cord-v1",
			    "naver-clova-ix/donut-base-finetuned-cord-v1-2560",
			]:
						UpperCAmelCase__	:       int   =  '''<s_cord>'''
			elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
						UpperCAmelCase__	:       Optional[int]   =  '''s_cord-v2>'''
			elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
						UpperCAmelCase__	:       str   =  '''<s_zhtrainticket>'''
			elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
						# use a random prompt
						UpperCAmelCase__	:       Union[str, Any]   =  '''hello world'''
			else:
						raise ValueError("""Model name not supported"""       )
			UpperCAmelCase__	:       Any   =  original_model.decoder.tokenizer(lowerCamelCase_			,   add_special_tokens=lowerCamelCase_			,   return_tensors="""pt"""       )[
			    '''input_ids'''
			]

			UpperCAmelCase__	:       Optional[Any]   =  original_model.encoder.model.patch_embed(lowerCamelCase_       )
			UpperCAmelCase__	:       List[str]   =  model.encoder.embeddings(lowerCamelCase_       )
			assert torch.allclose(lowerCamelCase_			,   lowerCamelCase_			,   atol=1e-3       )

			# verify encoder hidden states
			UpperCAmelCase__	:       int   =  original_model.encoder(lowerCamelCase_       )
			UpperCAmelCase__	:       Tuple   =  model.encoder(lowerCamelCase_       ).last_hidden_state
			assert torch.allclose(lowerCamelCase_			,   lowerCamelCase_			,   atol=1e-2       )

			# verify decoder hidden states
			UpperCAmelCase__	:       List[Any]   =  original_model(lowerCamelCase_			,   lowerCamelCase_			,   lowerCamelCase_       ).logits
			UpperCAmelCase__	:       List[str]   =  model(lowerCamelCase_			,   decoder_input_ids=lowerCamelCase_       ).logits
			assert torch.allclose(lowerCamelCase_			,   lowerCamelCase_			,   atol=1e-3       )
			print("""Looks ok!"""       )

			if pytorch_dump_folder_path is not None:
						print(f'''Saving model and processor to {pytorch_dump_folder_path}'''       )
						model.save_pretrained(lowerCamelCase_       )
						processor.save_pretrained(lowerCamelCase_       )

			if push_to_hub:
						model.push_to_hub("""nielsr/""" + model_name.split("""/"""       )[-1]			,   commit_message="""Update model"""       )
						processor.push_to_hub("""nielsr/""" + model_name.split("""/"""       )[-1]			,   commit_message="""Update model"""       )


if __name__ == "__main__":
		__A						=argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--model_name',
		    default='naver-clova-ix/donut-base-finetuned-docvqa',
		    required=False,
		    type=str,
		    help='Name of the original model you\'d like to convert.',
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path',
		    default=None,
		    required=False,
		    type=str,
		    help='Path to the output PyTorch model directory.',
		)
		parser.add_argument(
		    '--push_to_hub',
		    action='store_true',
		    help='Whether or not to push the converted model and processor to the 🤗 hub.',
		)

		__A						=parser.parse_args()
		convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


import string
from math import logaa






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_		):
							A      :					List[str]      		=					document.translate(
							    str.maketrans(''''''     ,				''''''     ,				string.punctuation		)		).replace('''\n'''     ,				''''''		)
							A      :					Optional[int]      		=					document_without_punctuation.split(''' '''		)  # word tokenization
							return len([word for word in tokenize_document if word.lower() == term.lower()]		)






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_		):
							A      :					Dict      		=					corpus.lower().translate(
							    str.maketrans(''''''     ,				''''''     ,				string.punctuation		)		)  # strip all punctuation and replace it with ''
							A      :					Dict      		=					corpus_without_punctuation.split('''\n'''		)
							A      :					List[Any]      		=					term.lower()
							return (len([doc for doc in docs if term in doc]		), len(lowerCamelCase_		))






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_=False		):
							if smoothing:
														if n == 0:
																					raise ValueError('''log10(0) is undefined.'''		)
														return round(1 + logaa(n / (1 + df)		)     ,				3		)

							if df == 0:
														raise ZeroDivisionError('''df must be > 0'''		)
							elif n == 0:
														raise ValueError('''log10(0) is undefined.'''		)
							return round(logaa(n / df		)     ,				3		)






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_		):
							return round(tf * idf     ,				3		)






